Instructions to use hf-tiny-model-private/tiny-random-Wav2Vec2Model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-tiny-model-private/tiny-random-Wav2Vec2Model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hf-tiny-model-private/tiny-random-Wav2Vec2Model")# Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("hf-tiny-model-private/tiny-random-Wav2Vec2Model") model = AutoModel.from_pretrained("hf-tiny-model-private/tiny-random-Wav2Vec2Model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e242732762c445034982bf3043f9d5c0059c722792198b9dbff7a4b2a118a8c5
- Size of remote file:
- 132 kB
- SHA256:
- e254b7f522ede628352eb1d13b1d203d48e200dccf749a3b51ac378142a6ea83
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